PosterText: Unifying Text Patch Generation and Editing for E-Commerce Poster Design

Automated e-commerce poster design requires not only the generation of high-quality posters from scratch but also the ability to flexibly and precisely edit existing designs. However, most existing methods either focus on end-to-end poster generation or follow multi-stage design pipelines, with limited capability for flexible and fine-grained editing of existing posters. To enable unified generation and editing of e-commerce posters, we introduce Text Patch Generation and Editing, a unified task formulation that treats text patches as atomic units and covers four fundamental operations: poster generation, patch addition, patch deletion, and patch modification, with optional reference-guided style condition control. Based on this formulation, we propose PosterText, a unified model trained with a four-stage curriculum, including text rendering pretraining, instruction-following training, reinforcement learning for preference alignment, and spatial guidance self-distillation for execution refinement. To facilitate systematic training and evaluation, we further construct a large-scale dataset with fine-grained patch-level annotations, together with a comprehensive benchmark covering both generation and editing scenarios. Extensive experiments demonstrate that PosterText achieves competitive performance against existing generation and editing approaches, validating the effectiveness of the proposed framework.